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Frontiers of Computer Science

ISSN 2095-2228

ISSN 2095-2236(Online)

CN 10-1014/TP

邮发代号 80-970

2019 Impact Factor: 1.275

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Frontiers of Computer Science  2024, Vol. 18 Issue (5): 185207   https://doi.org/10.1007/s11704-023-2771-z
  本期目录
Empirically revisiting and enhancing automatic classification of bug and non-bug issues
Zhong LI1,2, Minxue PAN1,3(), Yu PEI4, Tian ZHANG1,2, Linzhang WANG1,2, Xuandong LI1,2
1. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China
2. Department of Computer Science and Technology, Nanjing University, Nanjing 210023, China
3. Software Institute, Nanjing University, Nanjing 210093, China
4. Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China
 全文: PDF(4767 KB)   HTML
Abstract：

A large body of research effort has been dedicated to automated issue classification for Issue Tracking Systems (ITSs). Although the existing approaches have shown promising performance, the different design choices, including the different textual fields, feature representation methods and machine learning algorithms adopted by existing approaches, have not been comprehensively compared and analyzed. To fill this gap, we perform the first extensive study of automated issue classification on 9 state-of-the-art issue classification approaches. Our experimental results on the widely studied dataset reveal multiple practical guidelines for automated issue classification, including: (1) Training separate models for the issue titles and descriptions and then combining these two models tend to achieve better performance for issue classification; (2) Word embedding with Long Short-Term Memory (LSTM) can better extract features from the textual fields in the issues, and hence, lead to better issue classification models; (3) There exist certain terms in the textual fields that are helpful for building more discriminating classifiers between bug and non-bug issues; (4) The performance of the issue classification model is not sensitive to the choices of ML algorithms. Based on our study outcomes, we further propose an advanced issue classification approach, DEEPLABEL, which can achieve better performance compared with the existing issue classification approaches.
Key words： issue tracking    issue type prediction    empirical study
收稿日期: 2022-12-19      出版日期: 2023-07-10
Corresponding Author(s): Minxue PAN   
 引用本文:   
. [J]. Frontiers of Computer Science, 2024, 18(5): 185207.
Zhong LI, Minxue PAN, Yu PEI, Tian ZHANG, Linzhang WANG, Xuandong LI. Empirically revisiting and enhancing automatic classification of bug and non-bug issues. Front. Comput. Sci., 2024, 18(5): 185207.
 链接本文:  
https://academic.hep.com.cn/fcs/CN/10.1007/s11704-023-2771-z
https://academic.hep.com.cn/fcs/CN/Y2024/V18/I5/185207
Fig.1  
Fig.2  
Project # of issue # of bugs # of non-bugs
Jackrabbit 2,402 938 1,464
Lucene 2,443 697 1,746
HttpClient 746 305 441
Total 5,591 1,940 3,651
Tab.1  
Approach name Textual field x FRM for fext ML algorithm for fcls Original dataset Metrics
Antoniol Description TFM NB,LR,DT Own Dataset Precision and recall for each issue type
Chawla Title TFM Fuzzy Logic HerzigDataset Precision, recall and F1 score in the granularity of micro-averaging
Pandey TFM NB, SVM, LR
Otoom Reduced TFM NB,SVM,RF Own Dataset Accuracy
Terdchanakul The concatenation of title and description n-gram IDF LR,RF HerzigDataset F1 score in the granularity of micro-averaging
Pingclasai Topic Modeling DT,NB,LR
Qin Word Embedding with LSTM Softmax Layer
Kallis fastText fastText Own Dataset Precision, recall and F1 score in the granularity of micro-averaging
Herbold Using separate models for title and description fastText fastText HerzigDataset
Tab.2  
Fig.3  
Approach Bug Non-Bug Overall
precision recall F1 precision recall F1 precision recall F1
Chawla 0.73 0.49 0.59 0.77 0.90 0.83 0.76 0.76 0.75
Pandey 0.78 0.70 0.74 0.85 0.89 0.87 0.83 0.83 0.83
Otoom 0.24 0.03 0.05 0.64 0.94 0.76 0.51 0.63 0.52
Terdchanakul 0.58 0.33 0.42 0.71 0.87 0.78 0.67 0.68 0.66
Pingclasai 0.41 0.41 0.41 0.69 0.69 0.69 0.60 0.60 0.60
Qin 0.78 0.75 0.76 0.87 0.87 0.87 0.84 0.84 0.84
Kallis 0.78 0.71 0.74 0.85 0.88 0.87 0.83 0.83 0.83
Herbold 0.82 0.72 0.77 0.86 0.91 0.89 0.85 0.85 0.85
Tab.3  
Approach Strategy Bug Non-Bug Overall
precision recall F1 precision recall F1 precision recall F1
Chawla Title 0.73 (AB) 0.49 (A) 0.59 (A) 0.77 (A) 0.90 (B) 0.83 (A) 0.76 (A) 0.76 (A) 0.75 (A)
Desc 0.59 (C) 0.13 (B) 0.22 (B) 0.67 (B) 0.94 (A) 0.78 (B) 0.64 (B) 0.66 (B) 0.59 (B)
Comb1 0.64 (B) 0.14 (B) 0.22 (B) 0.67 (B) 0.95 (A) 0.79 (AB) 0.66 (B) 0.67 (B) 0.59 (B)
Comb2 0.75 (A) 0.50 (A) 0.60 (A) 0.77 (A) 0.91 (B) 0.83 (A) 0.77 (A) 0.77 (A) 0.76 (A)
Pandey Title 0.78 (A) 0.70 (A) 0.74 (A) 0.85 (A) 0.89 (A) 0.87 (A) 0.83 (A) 0.83 (A) 0.83 (A)
Desc 0.70 (C) 0.63 (B) 0.66 (C) 0.81 (B) 0.85 (B) 0.83 (C) 0.77 (B) 0.78 (B) 0.77 (B)
Comb1 0.74 (B) 0.72 (A) 0.73 (B) 0.85 (A) 0.86 (B) 0.86 (B) 0.81 (AB) 0.81 (AB) 0.81 (AB)
Comb2 0.79 (A) 0.71 (A) 0.75 (A) 0.85 (B) 0.90 (A) 0.87 (A) 0.84 (A) 0.84 (A) 0.84 (A)
Otoom Title 0.24 (B) 0.03 (?) 0.05 (B) 0.64 (?) 0.94 (?) 0.76 (?) 0.51 (?) 0.63 (?) 0.52 (?)
Desc 0.28 (A) 0.02 (?) 0.05 (B) 0.65 (?) 0.95 (?) 0.77 (?) 0.52 (?) 0.63 (?) 0.52 (?)
Comb1 0.27 (A) 0.05 (?) 0.09 (A) 0.65 (?) 0.92 (?) 0.75 (?) 0.52 (?) 0.62 (?) 0.53 (?)
Comb2 0.25 (B) 0.03 (?) 0.05 (B) 0.65 (?) 0.96 (?) 0.77 (?) 0.52 (?) 0.64 (?) 0.53 (?)
Terdchanakul Title 0.66 (B) 0.30 (B) 0.41 (?) 0.71 (?) 0.92 (A) 0.80 (A) 0.70 (A) 0.70(B) 0.67 (B)
Desc 0.58 (C) 0.35 (A) 0.43 (?) 0.71 (?) 0.86 (B) 0.78 (B) 0.67 (B) 0.69 (B) 0.66 (B)
Comb1 0.58 (C) 0.33 (A) 0.42 (?) 0.71 (?) 0.87 (B) 0.78 (B) 0.67 (B) 0.68 (B) 0.66 (B)
Comb2 0.69 (A) 0.29 (B) 0.40 (?) 0.71 (?) 0.93 (A) 0.81 (A) 0.71 (A) 0.71 (A) 0.70 (A)
Pingclasai Title 0.50 (B) 0.12 (B) 0.19 (C) 0.66 (?) 0.93 (A) 0.77 (B) 0.61 (B) 0.65 (B) 0.57 (C)
Desc 0.42 (C) 0.37 (A) 0.39 (A) 0.68 (?) 0.73 (B) 0.70 (C) 0.60 (B) 0.61 (C) 0.60 (B)
Comb1 0.41 (C) 0.41 (A) 0.41 (A) 0.69 (?) 0.69 (B) 0.69 (C) 0.60 (B) 0.60 (C) 0.60 (B)
Comb2 0.66 (A) 0.17 (B) 0.27 (B) 0.68 (?) 0.95 (A) 0.80 (A) 0.68 (A) 0.68 (A) 0.62 (A)
Qin Ttitle 0.77 (B) 0.72 (B) 0.74 (B) 0.86 (A) 0.88 (B) 0.87 (B) 0.83 (AB) 0.83 (AB) 0.83 (AB)
Desc 0.71 (C) 0.70 (C) 0.70 (C) 0.84 (B) 0.84 (C) 0.84 (C) 0.80 (B) 0.79 (B) 0.79 (B)
Comb1 0.78 (B) 0.75 (A) 0.76 (A) 0.87 (A) 0.87 (B) 0.87 (B) 0.84 (A) 0.84 (A) 0.84 (A)
Comb2 0.82 (A) 0.74 (A) 0.77 (A) 0.87 (A) 0.90 (A) 0.89 (A) 0.85 (A) 0.85 (A) 0.85 (A)
Kallis Title 0.77 (B) 0.72 (A) 0.74 (B) 0.86 (A) 0.88 (B) 0.87 (B) 0.83 (B) 0.83 (B) 0.83 (B)
Desc 0.74 (C) 0.62 (B) 0.67 (C) 0.81 (B) 0.88 (B) 0.84 (C) 0.79 (C) 0.79 (C) 0.79 (C)
Comb1 0.78 (B) 0.71 (A) 0.74 (B) 0.85 (A) 0.88 (B) 0.87 (B) 0.83 (B) 0.83 (B) 0.83 (B)
Comb2 0.82 (A) 0.72 (A) 0.77 (A) 0.86 (A) 0.91 (A) 0.89 (A) 0.85 (A) 0.85 (A) 0.85 (A)
Tab.4  
Fig.4  
Fig.5  
Fig.6  
Fig.7  
Classifier FRM Bug Non-Bug Overall
precision recall F1 precision recall F1 precision recall F1
LR word embedding with LSTM 0.82 (A) 0.74 (A) 0.78 (A) 0.87 (A) 0.91 (B) 0.89 (A) 0.85 (A) 0.85 (A) 0.85 (A)
fastText 0.73 (C) 0.38 (B) 0.50 (B) 0.74 (B) 0.93 (B) 0.82 (B) 0.74 (B) 0.74 (B) 0.71 (B)
TFM 0.80 (B) 0.72 (A) 0.76 (A) 0.86 (A) 0.90 (C) 0.88 (A) 0.84 (A) 0.84 (A) 0.84 (A)
reduced TFM 0.00 (E) 0.00 (E) 0.00 (E) 0.65 (C) 1.00 (A) 0.79 (C) 0.43 (D) 0.65 (C) 0.52 (D)
topic modeling 0.66 (D) 0.17 (D) 0.27 (D) 0.68 (BC) 0.94 (B) 0.79 (C) 0.68 (C) 0.68 (C) 0.61 (C)
n-gram IDF 0.72 (C) 0.25 (C) 0.37 (C) 0.70 (BC) 0.94 (B) 0.80 (B) 0.71 (BC) 0.70 (BC) 0.66 (BC)
RF word embedding with LSTM 0.81 (A) 0.74 (A) 0.78 (A) 0.87 (A) 0.91 (C) 0.89 (A) 0.85 (A) 0.85 (A) 0.85 (A)
fastText 0.80 (A) 0.18 (D) 0.30 (D) 0.69 (D) 0.97 (B) 0.80 (B) 0.73 (C) 0.70 (CD) 0.63 (D)
TFM 0.81 (A) 0.65 (B) 0.72 (B) 0.83 (B) 0.92 (C) 0.87 (A) 0.83 (B) 0.83 (B) 0.82 (B)
reduced TFM 0.28 (C) 0.00 (E) 0.00 (E) 0.65 (E) 0.99 (A) 0.78 (C) 0.54 (E) 0.65 (D) 0.51 (E)
topic modeling 0.49 (C) 0.23 (C) 0.32 (D) 0.68 (D) 0.87 (D) 0.76 (D) 0.62 (D) 0.65 (D) 0.61 (D)
n-gram IDF 0.69 (B) 0.28 (C) 0.40 (C) 0.71 (C) 0.93 (C) 0.80 (B) 0.70 (C) 0.71 (C) 0.67 (C)
DT word embedding with LSTM 0.65 (A) 0.86 (A) 0.74 (A) 0.91 (A) 0.75 (C) 0.82 (A) 0.82 (A) 0.79 (A) 0.79 (A)
fastText 0.41 (C) 0.66 (B) 0.50 (C) 0.73 (C) 0.49 (E) 0.59 (D) 0.62 (D) 0.55 (D) 0.56 (E)
TFM 0.57 (B) 0.84 (A) 0.68 (B) 0.88 (B) 0.67 (D) 0.76 (B) 0.78 (B) 0.73 (B) 0.73 (B)
reduced TFM 0.29 (D) 0.00 (E) 0.01 (E) 0.65 (D) 0.99 (A) 0.78 (AB) 0.53 (E) 0.64 (C) 0.51 (F)
topic modeling 0.41 (C) 0.40 (C) 0.40 (D) 0.68 (CD) 0.69 (D) 0.68 (C) 0.59 (DE) 0.59 (D) 0.59 (D)
n-gram IDF 0.68 (A) 0.28 (D) 0.39 (D) 0.71 (CD) 0.92 (B) 0.80 (AB) 0.70 (C) 0.70 (B) 0.66 (C)
Softmax word embedding with LSTM 0.81 (A) 0.73 (B) 0.77 (A) 0.86 (A) 0.90 (B) 0.88 (A) 0.85 (A) 0.85 (A) 0.85 (A)
fastText 0.33 (D) 0.94 (A) 0.49 (C) 0.32 (E) 0.01 (C) 0.02 (C) 0.33 (F) 0.33 (D) 0.18 (E)
TFM 0.80 (A) 0.64 (C) 0.71 (B) 0.82 (B) 0.91 (B) 0.87 (A) 0.82 (B) 0.82 (B) 0.81 (B)
reduced TFM 0.00 (E) 0.00 (E) 0.00 (E) 0.65 (D) 1.00 (A) 0.78 (B) 0.42 (E) 0.65 (C) 0.51 (D)
topic modeling 0.52 (C) 0.03 (E) 0.07 (E) 0.65 (D) 0.98 (A) 0.78 (B) 0.62 (D) 0.66 (C) 0.54 (D)
n-gram IDF 0.74 (B) 0.09 (D) 0.16 (D) 0.67 (C) 0.97 (AB) 0.79 (AB) 0.70 (C) 0.67 (C) 0.58 (C)
Tab.5  
Fig.8  
Fig.9  
FRM Classifier Bug Non-Bug Overall
precision recall F1 precision recall F1 precision recall F1
word embedding with LSTM LR 0.82 (A) 0.74 (B) 0.78 (A) 0.87 (B) 0.91 (A) 0.89 (A) 0.85 (A) 0.85 (A) 0.85 (A)
RF 0.81 (A) 0.74 (B) 0.78 (A) 0.87 (B) 0.91 (A) 0.89 (A) 0.85 (AB) 0.85 (A) 0.85 (A)
DT 0.65 (B) 0.86 (A) 0.74 (B) 0.91 (A) 0.75 (B) 0.82 (B) 0.82 (B) 0.79 (B) 0.79 (B)
Softmax 0.81 (A) 0.73 (B) 0.77 (AB) 0.86 (B) 0.90 (A) 0.88 (A) 0.85 (AB) 0.85 (A) 0.85 (A)
fastText LR 0.73 (B) 0.38 (C) 0.50 (A) 0.74 (A) 0.93 (B) 0.82 (A) 0.74 (A) 0.74 (A) 0.71 (A)
RF 0.80 (A) 0.18 (D) 0.30 (B) 0.69 (A) 0.97 (A) 0.80 (B) 0.73 (B) 0.70 (A) 0.63 (B)
DT 0.41 (C) 0.66 (B) 0.50 (A) 0.73 (A) 0.49 (C) 0.59 (C) 0.62 (C) 0.55 (B) 0.56 (C)
Softmax 0.33 (C) 0.94 (A) 0.49 (A) 0.32 (B) 0.01 (D) 0.02 (D) 0.33 (D) 0.33 (C) 0.18 (D)
TFM LR 0.80 (A) 0.72 (B) 0.76 (A) 0.86 (B) 0.90 (A) 0.88 (A) 0.84 (A) 0.84 (A) 0.84 (A)
RF 0.81 (A) 0.65 (C) 0.72 (AB) 0.83 (B) 0.92 (A) 0.87 (A) 0.83 (A) 0.83 (A) 0.82 (A)
DT 0.57 (B) 0.84 (A) 0.68 (B) 0.88 (A) 0.67 (B) 0.76 (B) 0.78 (B) 0.73 (B) 0.73 (B)
Softmax 0.80 (A) 0.64 (C) 0.71 (AB) 0.82 (B) 0.91 (A) 0.87 (A) 0.82 (AB) 0.82 (A) 0.81 (A)
reduced TFM LR 0.00 (B) 0.00 (B) 0.00 (B) 0.65 (?) 1.00 (A) 0.79 (?) 0.43 (B) 0.65 (?) 0.52 (?)
RF 0.28 (A) 0.00 (AB) 0.00 (AB) 0.65 (?) 0.99 (B) 0.78 (?) 0.54 (A) 0.65 (?) 0.51 (?)
DT 0.29 (A) 0.00 (A) 0.01 (A) 0.65 (?) 0.99 (C) 0.78 (?) 0.53 (A) 0.64 (?) 0.51 (?)
Softmax 0.00 (B) 0.00 (B) 0.00 (B) 0.65 (?) 1.00 (AB) 0.78 (?) 0.42 (B) 0.65 (?) 0.51 (?)
topic modeling LR 0.66 (A) 0.17 (C) 0.27 (B) 0.68 (?) 0.94 (B) 0.79 (A) 0.68 (A) 0.68 (A) 0.61 (A)
RF 0.49 (B) 0.23 (B) 0.32 (B) 0.68 (?) 0.87 (C) 0.76 (A) 0.62 (B) 0.65 (A) 0.61 (A)
DT 0.41 (B) 0.40 (A) 0.40 (A) 0.68 (?) 0.69 (D) 0.68 (B) 0.59 (B) 0.59 (B) 0.59 (AB)
Softmax 0.52 (AB) 0.03 (D) 0.07 (C) 0.65 (?) 0.98 (A) 0.78 (A) 0.62 (B) 0.66 (A) 0.54 (B)
n-gram IDF LR 0.72 (?) 0.25 (A) 0.37 (A) 0.70 (?) 0.94 (B) 0.80 (?) 0.71 (?) 0.70 (?) 0.66 (A)
RF 0.69 (?) 0.28 (A) 0.40 (A) 0.71 (?) 0.93 (B) 0.80 (?) 0.70 (?) 0.71 (?) 0.67 (A)
DT 0.68 (?) 0.28 (A) 0.39 (A) 0.71 (?) 0.92 (B) 0.80 (?) 0.70 (?) 0.70 (?) 0.66 (A)
Softmax 0.74 (?) 0.09 (B) 0.16 (B) 0.67 (?) 0.97 (A) 0.79 (?) 0.70 (?) 0.67 (?) 0.58 (B)
Tab.6  
Fig.10  
Fig.11  
Approach Merformance metrics Statistical testing
Bug Non-Bug Overall Bug Non-Bug Overall
precision recall F1 precision recall F1 precision recall F1 precision recall F1 precision recall F1 precision recall F1
Chawla 0.73 0.49 0.59 0.77 0.90 0.83 0.76 0.76 0.75 0.86 0.86 1.00 0.97 0.21 0.84 0.94 0.93 0.97
Pandey 0.78 0.70 0.74 0.85 0.89 0.87 0.83 0.83 0.83 0.49 0.54 0.67 0.58 0.30 0.40 0.69 0.65 0.67
Otoom 0.24 0.03 0.05 0.64 0.94 0.76 0.51 0.63 0.52 1.00 1.00 1.00 ?0.64 1.00 0.94 1.00 1.00 1.00
Terdchanakul 0.58 0.33 0.42 0.71 0.87 0.78 0.67 0.68 0.66 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Pingclasai 0.41 0.41 0.41 0.69 0.69 0.69 0.60 0.60 0.60 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
Qin 0.78 0.75 0.76 0.87 0.87 0.87 0.84 0.84 0.84 0.65 0.09 0.50 0.18 0.35 0.28 0.44 0.43 0.42
Kallis 0.78 0.71 0.74 0.85 0.88 0.87 0.83 0.83 0.83 0.48 0.44 0.68 0.50 0.26 0.40 0.63 0.58 0.60
Herbold 0.82 0.72 0.77 0.86 0.91 0.89 0.85 0.85 0.85 0.00 0.47 0.38 0.38 ?0.05 0.16 0.30 0.29 0.30
BERT-Title 0.77 0.78 0.77 0.88 0.87 0.87 0.84 0.84 0.84 0.62 ?0.20 0.28 0.02 0.48 0.30 0.32 0.38 0.31
BERT-Desc 0.72 0.70 0.71 0.84 0.85 0.84 0.80 0.80 0.80 0.88 0.56 0.90 0.66 0.66 0.70 0.82 0.82 0.82
BERT-Comb1 0.78 0.79 0.78 0.89 0.88 0.88 0.85 0.85 0.85 0.42 ?0.34 0.16 ?0.10 0.42 0.16 0.16 0.19 0.15
DEEPLABEL 0.82 0.77 0.79 0.88 0.91 0.89 0.86 0.86 0.86 ?
Tab.7  
Fig.12  
ITS # of issue # of porjects # of bugs
Jira Apache 879,602 620 467,069
JBoss 354,852 418 172,104
Spring 66,404 95 26,163
GitHub 184,834 50 75,277
Total 1,485,693 1,183 740,613
Tab.8  
Approach Performance metrics Statistical testing
Bug Non-Bug Overall Bug Non-Bug Overall
precision recall F1 precision recall F1 precision recall F1 precision recall F1 precision recall F1 precision recall F1
Chawla 0.79 0.61 0.67 0.69 0.82 0.72 0.75 0.70 0.69 0.36 0.80 1.00 0.80 ?0.70 0.80 1.00 1.00 1.00
Pandey 0.80 0.74 0.77 0.75 0.82 0.78 0.78 0.78 0.78 0.68 1.00 1.00 0.80 ?0.24 0.62 1.00 1.00 1.00
Otoom 0.56 0.90 0.69 0.76 0.29 0.42 0.66 0.59 0.55 1.00 ?0.88 1.00 0.8 1.00 1.00 1.00 1.00 1.00
Terdchanakul 0.67 0.61 0.63 0.64 0.70 0.66 0.67 0.65 0.65 1.00 1.00 1.00 1.00 0.84 1.00 1.00 1.00 1.00
Pingclasai 0.71 0.68 0.69 0.67 0.71 0.69 0.70 0.69 0.69 1.00 1.00 1.00 0.96 0.90 1.00 1.00 1.00 1.00
Qin 0.82 0.84 0.83 0.83 0.82 0.82 0.82 0.82 0.82 0.17 0.28 0.43 ?0.12 ?0.25 ?0.17 0.26 0.23 0.24
Kallis 0.79 0.82 0.80 0.81 0.78 0.80 0.80 0.80 0.80 0.78 0.56 0.78 0.29 0.42 0.39 0.76 0.80 0.80
Herbold 0.81 0.84 0.82 0.82 0.79 0.80 0.81 0.81 0.81 0.48 0.34 0.52 0.27 0.44 0.43 0.58 0.62 0.60
BERT-Title 0.81 0.83 0.82 0.83 0.82 0.82 0.82 0.82 0.82 0.43 0.46 0.62 0.07 0.13 0.05 0.49 0.48 0.48
BERT-Desc 0.79 0.83 0.80 0.80 0.77 0.78 0.80 0.80 0.80 0.76 0.50 0.70 0.48 0.68 0.64 0.94 0.94 0.94
BERT-Comb1 0.82 0.85 0.84 0.84 0.83 0.84 0.83 0.83 0.83 0.2 0.18 0.34 ?0.27 ?0.44 ?0.20 0.32 0.32 0.32
DEEPLABEL 0.85 0.86 0.86 0.83 0.82 0.83 0.85 0.85 0.85 ?
Tab.9  
Fig.13  
Approach Overall performance Statistical testing
precision recall F1 precision recall F1
Pandey 0.66 0.64 0.63 ?0.16 0.18 0.19
Terdchanakul 0.31 0.38 0.27 1.00 1.00 1.00
Pingclasai 0.35 0.39 0.34 1.00 1.00 1.00
Qin 0.37 0.39 0.31 1.00 1.00 1.00
Kallis 0.48 0.56 0.48 1.00 0.98 1.00
Herbold 0.63 0.65 0.62 0.24 0.16 0.46
DEEPLABEL 0.65 0.66 0.65 ?
Tab.10  
Field Keywords
Title support, should, add, error, not, doesnt, fail, fix, update, remove, improve, wrong, upgrade, need, miss
Description support, current, add, fail, need, should, not, codeseg, would, hyperlink, improve, remove, fix, error, provide
Tab.11  
  
  
  
  
  
  
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